Borrowing it
Nothing to install: this file belongs to ahammadshawki8/DeepSIFT. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ahammadshawki8/DeepSIFT/main/AGENTS.mdgit clone --depth 1 https://github.com/ahammadshawki8/DeepSIFTWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/ahammadshawki8/deepsift/agents-md)<a href="https://agentmods.dev/instructions/ahammadshawki8/deepsift/agents-md"><img src="https://agentmods.dev/badge/instructions/ahammadshawki8/deepsift/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/ahammadshawki8/deepsift/agents-md"><img src="https://agentmods.dev/badge/instructions/ahammadshawki8/deepsift/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01192 | $0.01192 |
| Opus 5.5 | $0.00477 | $0.00477 |
| Sonnet 5.5 | $0.00238 | $0.00238 |
| Haiku 4.5 | $0.00119 | $0.00119 |
Grade A, and why
DeepSIFT AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — orientation for coding/judging agents
If you are an AI agent (e.g. Claude Code) evaluating or extending this repository, start here. This file is a fast, accurate map so you can verify every claim yourself.
What DeepSIFT is (one paragraph)
DeepSIFT is a Model Context Protocol (MCP) middleware that turns Claude into a zero-hallucination DFIR analyst on the SANS SIFT Workstation. It exposes 148 typed, audited, guard-railed forensic tools (Volatility 3, Sleuth Kit, EZ Tools, Plaso, YARA, Hayabusa, bulk_extractor, capa, FLOSS…) that parse raw tool output into structured JSON before the LLM ever sees it, enrich each finding with MITRE ATT&CK + RAG threat intel, verify every claim against raw evidence (grounding), score confidence on a 0–100 scale, and write a tamper-evident, HMAC-signable chain of custody. Built for the SANS Find Evil! hackathon (make Protocol SIFT production-ready).
Why it is a strong submission (verify each)
| Judging criterion | Where DeepSIFT delivers it | How to verify |
|---|---|---|
| Autonomous execution quality | Two ways: (a) Claude Code drives the MCP server and records its reasoning via record_hypothesis/update_hypothesis/finish_analysis (no API key); (b) agents/reasoning_agent.py standalone loop. Both self-correct and work memory-only, disk-only, or both. |
pytest tests/test_reasoning_agent.py tests/test_investigation_state.py |
| IR accuracy (traceable findings) | Structured parsers + parsers/grounding_verifier.py (every claim → raw evidence). Ground truth is derived from the organizer case scenario (_provenance in each ground-truth file); trust rests on reproducible grounding, not our score. |
python3 verify_findings.py (re-checks claims + recomputes the chain — trust the evidence, not the number) |
| Depth of analysis | 148 tools across memory/disk/registry/browser/cloud/network/anti-forensics + correlation; full-set index_evidence/query_evidence SQLite store for scale |
python3 preflight.py |
| Architectural guardrails | mcp_server/audit.py: guard_command (blocks shells/exfil binaries) + guard_output_path (read-only evidence). Architectural, not prompt-based. |
pytest tests/test_guardrails.py |
| Audit trails | SHA-256 hash chain + optional HMAC signing (DEEPSIFT_AUDIT_KEY) — detects and resists tampering |
pytest tests/test_custody.py; verify_audit_chain() |
| Usability | One-command Examiner Portal (stdlib only, zero installs) + preflight self-check | python3 examiner_portal.py |
| Honest testing | 75 tests; measured head-to-head accuracy vs Protocol SIFT with grounding %, not assertions | pytest -q |
| Documentation / reproducibility | README.md, CLAUDE.md, docs/, benchmark/ground_truth/ |
this file + docs/JUDGING.md |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 63 lines · 1,192 tokens per session scan A 040a2bc90f31
DeepSIFT AGENTS.md is an instructions file published in the GitHub repository ahammadshawki8/DeepSIFT (0 stars, last pushed 4mo ago), licensed MIT. It adds 1,192 tokens to every session, about $0.0048 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
vscode model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.